Abstract
The issue of crack detection and its diagnosis has gained a wide spread of industrial interest. The crack/damage affects the industrial economic growth. So early crack detection is an important aspect in the point of view of any industrial growth. In this paper a design tool ANSYS is used to monitor various changes in vibrational characteristics of thin transverse cracks on a cantilever beam for detecting the crack position and depth and was compared using artificial intelligence techniques. The usage of neural networks is the key point of development in this paper. The three neural networks used are cascade forward back propagation (CFBP) network, feed forward back propagation (FFBP) network, and radial basis function (RBF) network. In the first phase of this paper theoretical analysis has been made and then the finite element analysis has been carried out using commercial software, ANSYS. In the second phase of this paper the neural networks are trained using the values obtained from a simulated model of the actual cantilever beam using ANSYS. At the last phase a comparative study has been made between the data obtained from neural network technique and finite element analysis.
Highlights
The presence of cracks in a mechanical member has catastrophic effects on its functionality and the failure in detecting the said cracks may result in failure of the mechanical member
This network differs from the cascade forward back propagation (CFBP) network on the basis that each subsequent layer has a weight coming from the previous layer and no connection is made between the layers and the first layer
Finite element modeling is applied in the cracked beam using ANSYS to monitor the various frequencies in different modes of crack formation
Summary
The presence of cracks in a mechanical member has catastrophic effects on its functionality and the failure in detecting the said cracks may result in failure of the mechanical member. Lifshitz and Rotem [1] pioneered the proposed damage detection via vibration measurements-inverse measurement of crack parameters from vibrational parameters They look at the change in the dynamic moduli, which can be related to the frequency shift, as indicating damage in particle-filled elastomers. Suresh et al [15] presented a method by considering the flexural vibration in a cantilever beam with a transverse crack They computed modal frequency parameters analytically for various crack locations and depths. In the present study a methodical approach to analyze and detect the crack parameters, that is, the crack location and relative crack depth from a set of frequency values obtained from a simulated model of the cantilever beam containing a thin transverse crack using ANSYS. Cascade forward back propagation (CFBP), feed forward back propagation (FFBP) and radial basis function (RBF) neural network models are developed and result obtained are compared
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